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212 lines
6.6 KiB
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212 lines
6.6 KiB
Markdown
---
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id: wiki-2026-0508-growth-mindset
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title: Growth Mindset
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [growth mindset, fixed mindset, Carol Dweck, mindset theory, intelligence beliefs]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [psychology, education, mindset, dweck, intervention, effort]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Psychology / Education
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applicable_to: [Education, Coaching, Workplace]
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---
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# Growth Mindset
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## 매 한 줄
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> **"매 ability 의 의 의 develop 의 가능 의 belief"**. Carol Dweck (Stanford). 매 fixed (talent fixed) ↔ growth (effort + strategy 의 의 의 grow). 매 modern: 매 effect size 의 small but real (Sisk meta-analysis 2018), 매 implementation 의 critical.
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## 매 핵심
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### 매 vs fixed
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- **Fixed**: 매 ability is innate, 매 effort = lack of talent.
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- **Growth**: 매 ability develops, 매 effort + strategy = path.
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- **Hybrid** (most people): 매 domain-specific.
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### 매 evidence
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- **Mueller & Dweck 1998**: 매 praise effort vs ability.
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- **Yeager 2019** (NSLM): 매 large RCT — 매 9th-grader effect on low-achievers.
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- **Sisk meta-analysis 2018**: 매 effect size 의 small but reliable.
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- **Replication concerns** (Macnamara 2018).
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### 매 modern nuance
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- **Not magic**: 매 effort ≠ outcome alone.
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- **Strategy matters**: 매 deliberate practice.
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- **Context matters**: 매 environment 의 fix barriers first.
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- **Praise process** (specific) 의 of 매 ability.
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### 매 응용
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1. **Education**: 매 student.
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2. **Workplace**: 매 manager feedback.
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3. **Sports / arts**: 매 coaching.
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4. **Therapy** (CBT-related).
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5. **Parenting**.
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## 💻 패턴
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### Praise pattern
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```python
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# 매 ❌ Fixed (ability praise)
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"You're so smart!"
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# 매 ✅ Growth (process praise)
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"I can see you tried different strategies on that problem."
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"Your effort paid off — you stuck with it."
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"What strategy worked? What would you try next?"
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```
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### Self-talk reframe
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```python
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FIXED_TO_GROWTH = {
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"I can't do this.": "I can't do this YET.",
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"I'm not good at this.": "What am I missing?",
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"I made a mistake.": "Mistakes help me learn.",
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"It's good enough.": "Is this really my best work?",
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"I'll never be as smart as her.": "I'll figure out what she does.",
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}
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```
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### Implementation intention
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```python
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def growth_mindset_intent(challenge):
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return f"When I encounter {challenge}, I will: try a new strategy, ask for feedback, see it as an opportunity."
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```
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### Effort + strategy track
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```python
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class LearningJournal:
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def log(self, task, effort_min, strategy_used, outcome, lesson):
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self.entries.append({
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'task': task, 'effort': effort_min,
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'strategy': strategy_used, 'outcome': outcome,
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'lesson': lesson,
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})
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def reflect(self):
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# 매 매 strategy 의 의 outcome 의 correlate
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return analyze_strategy_outcome(self.entries)
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```
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### Yet (powerful word)
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```python
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def add_yet(statement):
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if statement.endswith("can't") or statement.endswith("don't"):
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return statement + " yet"
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return statement
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```
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### Feedback rubric
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```yaml
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process_feedback:
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- specific: "You broke the problem into steps before solving"
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- effort: "You spent time understanding before answering"
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- strategy: "Try writing it out next time"
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- growth: "Last week you struggled with this — now you got it"
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avoid:
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- global ability: "You're a math genius"
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- person: "You're so smart"
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```
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### Brainology-style intervention
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```python
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def neuroplasticity_lesson():
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return """
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The brain is like a muscle. Every time you tackle a hard problem:
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1. Neurons fire and form new connections.
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2. The connections get stronger with practice.
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3. Mistakes ARE learning — that's where growth happens.
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4. Your brain physically changes when you learn.
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"""
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```
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### School climate intervention (Yeager NSLM)
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```python
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def nslm_session():
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"""매 ~50 min — 매 RCT-validated."""
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return [
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('intro_brain_grows', 5),
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('story_struggling_student_grew', 10),
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('exercise_my_struggle_strategy', 15),
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('write_letter_advice_to_struggling_peer', 15),
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('reflection', 5),
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]
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```
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### Track (per-student)
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```python
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def mindset_score(student_responses):
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"""매 Dweck Mindset Quiz — 매 8 items, Likert 1-6."""
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growth_items = student_responses[::2] # 매 even
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fixed_items = student_responses[1::2] # 매 odd reverse
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return (sum(growth_items) + sum(7 - x for x in fixed_items)) / 8
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```
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### Workplace manager script
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```python
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GROWTH_FEEDBACK_TEMPLATES = [
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"I noticed you tried [strategy]. What did you learn?",
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"What's one challenge you want to take on this quarter?",
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"When this didn't go as planned, what's one thing you'd do differently?",
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"What did you struggle with this week? That's where the learning happens.",
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]
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```
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### Combine with deliberate practice
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```python
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def growth_practice_session(skill):
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return {
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'mindset': 'I can grow this skill with focused practice',
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'specific_subskill': identify_weakness(skill),
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'feedback_loop': 'recorded session, coach review',
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'effortful': 'just beyond current ability',
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'duration_min': 60,
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'reflection': 'what worked, what didn't, next iteration',
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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| K-12 student | NSLM-style intervention |
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| Workplace | Manager training + feedback rubric |
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| Therapy | CBT + reframe |
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| Self-coaching | Journal + yet |
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| Sports / arts | Coach feedback + deliberate practice |
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**기본값**: 매 process praise + yet + journal + manager training. 매 intervention 매 always pair with environmental support (resources, role models).
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## 🔗 Graph
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- 부모: [[Psychology]]
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- 변형: [[Growth-Mindset|Growth-Mindset-Intervention]]
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- 응용: [[Deliberate-Practice]]
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- Adjacent: [[Grit]] · [[Eudaimonia-and-Well-being]]
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## 🤖 LLM 활용
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**언제**: 매 education app. 매 coaching tool. 매 reflection journal.
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**언제 X**: 매 toxic positivity 의 risk.
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## ❌ 안티패턴
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- **Effort-only obsession**: 매 strategy/feedback 의 ignore.
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- **Fixed mindset gaslighting**: 매 environment problem 의 attribute to mindset.
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- **Praise inflation**: 매 every effort = special.
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- **Single intervention**: 매 follow-up 의 X.
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## 🧪 검증 / 중복
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- Verified (Dweck 2006/2017, Yeager 2019, Sisk 2018, Macnamara 2018 critique).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-04-26 | Auto |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Dweck + 매 praise / yet / NSLM / journal code |
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